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One Uber's Marketplace team we're tasked with efficiently matching our riders and driver partners in real time. To that end, we we employ various systems within the ride sharing marketplace such as dynamic pricing (popularly known as surge), demand modeling and forecasting systems and health monitoring systems in order to ensure optimal marketplace efficiency. One of the key data systems underpinning Uber's Marketplace is the Rider Sessions pipeline which tracks the lifetime of a singular Uber trip in realtime, capturing rider interaction with various Uber systems, from the pricing engine to dispatch systems all the way until a trip end. In our talk we discuss the evolution of the Rider Session state machine at Uber and challenges involved in managing realtime stateful streaming pipeline across half a dozen event streams and millions of riders who use Uber every day. We plan to delve into various aspects of the running the job in production such as managing state checkpointing, monitoring, experiences moving the job from Spark Streaming to Flink and ensuring low latency to downstream systems.

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How Easy the AmiBroker AFL can be converted into C++ DLL plugin...! There are many AFL experts who are traders as well who want to convert their AFL code business(trade) logic to host inside the DLL plugin for hiding the logic and for the distribution to their clients... How easy is this for AFL coders?? Definitely tough and tedious job if you are directly using AmiBroker C based ADK..besides AFL coders may not know the C/C++ language. Here is one library description that can be used for 1-1 mapping each line in the AFL code to C/C++ code.. Refer the simple example attached which shows the Pivots AFL code and its equivalent C/C++ code. Compare the line numbers between 10 to 29 high-lighted in yellow color !!!... It is virtually "Cut n Paste" i.e. "Cut as AFL & Paste as C++" operation!!...

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Automating Copy Orders from MetaTrader to Interactive Brokers is a sophisticated trading solution that streamlines the process of executing trades across multiple platforms. This system enables seamless integration between MetaTrader, a popular forex trading platform, and Interactive Brokers, a renowned brokerage service. By employing this automation, traders can easily mirror trades from MetaTrader to their Interactive Brokers account without manual intervention. This integration allows for real-time synchronization of trades, ensuring accurate execution and minimizing delays. The system leverages advanced API capabilities to establish a secure and reliable connection between the two platforms, ensuring that trade signals are swiftly transmitted from MetaTrader to Interactive Brokers. This synchronization empowers traders to implement their strategies efficiently across different markets, asset classes, and account types. With this automation, traders can benefit from enhanced precision, reduced trading latency, and increased overall efficiency, leading to potential improvements in their trading performance and results. However, users should exercise due diligence and thoroughly understand the automation process to avoid any unintended consequences.

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Discover how to do model execution in Capella, and how to embed digital mockup inside functions to do 'system simulation' with a higher confidence. A common need in system architecture design is to verify that the architect is correct and can satisfy its requirements. Execution of system architect model means to interact with state machines to test system’s control logic. It can verify if the logical sequences of functions and interfaces in different scenarios are desired. However, only sequence itself is not enough to verify its consequence or output. So we need each function to do what it is supposed to do during model execution to verify its output. That's what we called 'system simulation'.

Model Execution and System Simulation

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A common need in system architecture design is to verify that if the architect is correct and can satisfy its requirements. Execution of system architect model means to interact with state machines to test system’s control logic. It can verify if the logical sequences of functions and interfaces in different scenarios are desired. However, only sequence itself is not enough to verify its consequence or output. So we need each function to do what it is supposed to do during model execution to verify its output, and that is what we called “system simulation”. This presentation introduces how we do model execution in Capella, and how to embed digital mockup inside functions to do “system simulation” with a higher confidence. Renfei Xu, Glaway Renfei Xu is the technical manager of MBSE solution in Glaway. He has participated in many pilot projects of MBSE in areas like Engine Control, Avionics, Mechatronics and so on. In recent years, he is responsible for the deployment of MBSE using Capella and ARCADIA methodology in a Radar research institute. Wenhua Fang, Glaway Wenhua Fang is the Director of Systems Engineering in Glaway. He has more than 12 years of working experience in SE. He is responsible for more than 10 implementation projects of MBSE in areas like Aircraft, Engine Control, Avionics, Automotive and so on. In recent years, he leads the team to deploy MBSE in China(including using Capella and ARCADIA methodology).

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4-5 May 2022 IEEE Computational Intelligence for Financial Engineering and Economics Instability of financial markets by optimizing investment strategies investigated by an agent-based model Takanobu Mizuta SPARX Asset Management Co. Ltd. Isao Yagi Kogakuin University Kosei Takashima Nagaoka University Note that the opinions contained herein are solely those of the authors and do not necessarily reflect those of SPARX Asset Management Co., Ltd. In this study, we built an artificial market model by adding technical analysis strategy agents (TAs), which search one optimized parameter in a whole simulation run, to the prior model of [mizuta 2016]. The TAs are a momentum TA (TA-m) and reversal TA (TA-r), and we investigated whether investors' inability to accurately estimate market impacts in their optimizations leads to optimization instability. When both the TA-m and TA-r exist, the parameters of investment strategies were changing irregularly and unexpectedly. This means that even if all other traders are fixed, only one investor optimizing his/her strategy using backtesting leads to the time evolution of market prices becoming unstable. Financial markets are essentially unstable, and naturally, investment strategies are not able to be fixed. The reason is that even when one investor selects a rational strategy at that time, it changes the time evolution of prices, it becomes no longer rational, another strategy becomes rational, and the process repeats. Optimization instability is one level higher than ``non-equilibrium of market prices.'' Therefore, the time evolution of market prices produced by investment strategies having such unstable parameters is highly unlikely to be predicted and have stable laws written by equations. This nature makes us suspect that financial markets include the principle of natural uniformity and indicates the difficulty of building an equation model explaining the time evolution of prices.

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